From one target to many: how high‑plex ddPCR technology is changing CGT from development to analytics

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Multiplexing in qPCR has long been considered technically challenging to scale reliably. What are the underlying reasons for this?

CO: Multiplexing in qPCR has long been considered technically challenging, and it is not something commonly seen in that area of the business. When you reach three or more targets, it becomes nearly impossible. The primary reason is that when you start combining more than one assay into a single well, interplay between assay components, including primers and probes from different assays, will begin to bind with each other, causing lower reaction efficiency. Competition for binding sites in the case of Single‑nucleotide polymorphism (SNP) assays is also a larger driver of inefficiency, Finally competition for other reaction components including polymerase, dNTPs, and other co‑factors also reduces reaction efficiency. This interplay between oligonucleotides and reaction components can affect the quantification and sensitivity of your assays.

Droplet digital PCR™ (ddPCR™) operates on a fundamentally different principle. Can you explain the distinction, and how does that change what is achievable in higher‑plex assay design?

CO: The way I like to consider this is that ddPCR takes a single bulk qPCR‑like reaction and partitions it into tens of thousands of individual nanomicro‑reactions. What happens in one droplet does not affect what happens in neighboring droplets.

From a quantification perspective, qPCR monitors cycles, broadly, how long it takes for a sample to become detectable. When you have those slight interplays that push efficiencies below the range that the Minimum Information for Publication of Quantitative Real‑Time PCR Experiments (MIQE) guidelines recommend – somewhere between 95 and 102% – quantification decreases, quantities fall, and limits of detection (LODs) and lower limits of quantification (LLOQs) are diminished.

ddPCR, on the other hand, is endpoint PCR. Rather than tracking when or how quickly the reaction amplifies, what we are really concerned with is the ratio of positive to negative droplets. We see these positive and negative bands or populations, and when efficiency decreases, those bands simply move slightly closer together. This indicates that we can take assays that could not meet the rigorous requirements for qPCR usage, port them over to ddPCR, and achieve very good results.

Where do labs typically encounter limitations when trying to operationalize high‑plex workflows?

CO: Multi‑assay optimization work requires expertise around assay design. Sequence complexity leading to inefficiencies, high GC content, repetitive sequences, and oligonucleotide interactions can burden the task. SNP assay development for qPCR is also very challenging, as these are not as straightforward gene expression assays. Besides the design‑related limitations, high‑plex qPCR raw data is difficult to analyze and may require advanced mathematical tools.

Because of these issues, dozens, possibly hundreds, of individual assays may have to be validated before a working high‑plex assay is found. For each of those, you will have to order a probe to accompany your primers, and the cost of those can run up to hundreds of dollars each, meaning you could spend thousands of dollars on validation before an assay even gets off the ground.

To counter these impediments around design, validation, and analysis, various approaches have emerged over the years. Bio‑Rad acquired a company formerly known as Stilla Technologies that uses a universal reporter system for its high‑plex (up to 21 targets in a single reaction) Crystal Digital PCR assays. This system removes the need for designing target‑specific fluorescent probes and replaces them with non‑fluorescent tagged probes, which take the most expensive part of the R&D or validation process and replaces it with much more affordable components. Moreover, because these probes carry no fluorophore, the same set of universal reporters can be reused across every assay, no matter what target you are looking for.

How did workflow speed previously limit gene expression studies?

CO: Large‑scale gene expression studies have historically carried enormous workloads, primarily driven by multiple targets of interest and reference genes. You take those totals and multiply them by sample numbers and replicate needs. Multiplexing reduces the number of runs or the number of wells required to gather results.

Additionally, data processing is an important part of these studies and is often overlooked. Gathering, processing, and collating data from dozens of plates that may contain identical or different samples is not a trivial undertaking. Add in interplate calibrators, standard curves, and delta‑delta Ct (ΔΔCt) calculations, and the data processing becomes monumental. Being able to efficiently multiplex without interplate calibrators or standard curves can reduce the workload and data processing required by orders of magnitude. In regulated environments where validated processes require human oversight for any manual calculations, this is a significant change. High order multiplexing combined with absolute quantification without a standard curve dramatically reduces the time users spend handling and processing data.

Why is copy number alone insufficient for cell therapy characterization?

CO: The location of a transgene cassette plays a key role in successful outcomes. Randomised integration of exogenous DNA can disrupt surrounding genes, and in some instances this leads to decreased or increased function and genomic instability. Depending on where the insert lands, tumor suppressors can be disrupted.

If you consider TP53 or BRCA genes, it can lead to proto‑oncogenes being activated. KRAS, BRAF, or MYC trigger genomic instability, driving rearrangements and leading to translocations and fusion protein formation. For example, fusions involving AML1, and several studies have leveraged this deliberately. The Sleeping Beauty transposon system, for instance, was used extensively to identify secondary and tertiary cancer drivers through forced insertional mutagenesis. Where an insert lands is therefore very important, as it can have unforeseen knock‑on effects. Copy number may drive expression and efficacy, but location is also a critical safety factor.

Vector integration analysis has historically relied on complex long‑range assays. How does a small amplicon multiplexing approach change that in practice?

CO: Long‑range PCR assays have been a common approach for measuring integration for many years. However, developing assays that need to amplify across kilobases of highly repetitive, GC‑rich sequence, such as those found in long terminal repeats (LTRs), is challenging and often produces poor‑quality results.

ddPCR excels here through what we consider linkage analysis. This approach was originally pioneered in fruit flies, before sequenced genomes were available, to determine the distance between 2 loci driving phenotypes by measuring recombination frequency across generations. It was later adapted into PCR‑based methods using forward and reverse primers separated by increasing distances, a technique known as mileposting. However, increasing amplicon size has its limits.

ddPCR uses linkage analysis to determine whether two efficient amplicons are physically linked to one another by evaluating the frequency with which they co‑localize to the same droplet. In ddPCR, if two targets are unlinked, they will distribute throughout the droplets at a frequency calculable using the Poisson distribution. When they deviate from that distribution, such as when co‑localization is higher than the calculated expectation, the system returns a value indicating how many of those copies are physically linked together.

In practice, you can take one amplicon targeting a known site in the genome and another within your gene of interest (GOI), and look for the linkage between them, without needing to design a long, complex assay that will perform poorly. Bio‑Rad software packages give you those calculations as a standard output. Next‑generation multiplexing goes further. It can describe linkage not just between one and two targets, but across multiple targets simultaneously: up to four for QX1, up to six for QX600, and up to seven for the new QX700 instruments.

Biodistribution studies typically involve a large number of targets across multiple tissue types. What does high‑plex gene expression profiling make possible that previously was not feasible at scale?

CO: Biodistribution studies are always an interesting topic. They are extremely costly to produce, and the samples tend to be very precious as they are limited in volume and time‑consuming to generate. Reducing the number of wells and the amount of sample consumed during each run is therefore an important factor.

If we can take 10–15 individual genes of interest and process those in 1–2 wells instead of 7–8 wells, we can dramatically reduce the amount of sample required. That is something of real interest to most researchers.

There appears to be a broader shift towards using next‑generation sequencing (NGS) for discovery and ddPCR for longitudinal monitoring. Is this a pattern you are observing, and what do you think is driving it?

CO: Yes, we frequently see researchers using this approach in the field. NGS and ddPCR are widely regarded as complementary technologies and are often used together: NGS for discovery, ddPCR for longitudinal monitoring moving forward.

The primary drivers for this shift appear to be much lower cost per sample with ddPCR analysis, faster turnaround with results on the day rather than days or weeks later, and reduced sample preparation, with no library generation required. There is also less data processing involved. Some users are cautious about black‑box data analysis, where the input and output are clear, but the processing in between is not. With ddPCR, you still get a raw visual output: you can see the positive and negative droplets and assess how things look, which provides a degree of interpretive transparency. This is something we see a great deal of in the oncology space.

As multiplexing capacity continues to develop, where do you see the most meaningful advances coming from?

CO: This is a multifaceted question. From a business perspective, drug development and clinical trials are probably the most important areas. ddPCR makes these processes faster and cost‑effective, which can lead to more discoveries and expedited approvals.

From a scientific perspective, I am excited about proteomics, specifically proteins in droplets. Proteomics has had something of a difficult time for a while. Genomics has benefited from PCR, NGS, the Human Genome Project, and a succession of remarkable technological advances over the past 40 years. On the proteomics side, we are still in many ways running antibodies onto membranes in a way that has not changed significantly since the 1970s. Being able to move proteomics technology into a more accessible and advanced platform, for example, to multiplex Western blot targets at high plex, is exciting.

And then, from the standpoint of broader impact, it always comes back to oncology, liquid biopsy, and minimal residual disease (MRD) monitoring. There are several off‑the‑shelf kits currently available for routine monitoring of various oncology markers – microsatellite instability (MSI), KRAS, and estrogen receptor (ESR) scanning kits, for example. These allow for routine, non‑invasive, fast, and cost‑effective screening, reducing the need for invasive biopsies and expensive imaging. ddPCR has the potential to advance research that may ultimately improve patient outcomes, and that is the key thing.

Biography

Christian Overgaard is an experienced Field Application Scientist (FAS) at Bio‑Rad Laboratories, supporting biopharma customers across the Southeast. He has supported academic, government, and biopharma customers across Bio‑Rad's genomics and proteomics portfolios. Dr Overgaard earned his doctorate in Anatomy and Cell Biology at the Carver College of Medicine at the University of Iowa and completed a postdoctoral fellowship at Emory University. His research focused on epithelial cell polarity in the lung and kidney.

Affiliation

Christian Overgaard PhD, Field Application Scientist, Bio‑Rad Laboratories

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Attribution: Copyright © 2026 Bio‑Rad. Published by Cell & Gene Therapy Insights under Creative Commons License Deed CC BY NC ND 4.0.

Article source: This article was developed by BioInsights’ Editorial team using expert insights shared during the podcast episode ‘From one target to many: how high‑plex ddPCR is changing CGT from development to analytics.’ This article is a reflection of the discussion held, edited for clarity and flow. The named contributor reviewed and approved the final version prior to publication.

Podcast Held: Jun 8, 2026.

Revised manuscript received: Jun 4, 2026.

Publication date: Jun 25, 2026.